ABSTRACT:
By increasing productivity, cutting expenses, and shortening turnaround times, the application of artificial intelligence (AI) and machine learning (ML) to drug discovery and development has completely transformed pharmaceutical research. From early target identification and lead discovery to clinical validation and the emergence of personalized medicine, this study examines the changing importance of machine learning across the drug development pipeline. In order to find molecular targets, optimize lead compounds, and anticipate toxicity profiles, traditional computational techniques like support vector machines, decision forests, and more contemporary deep learning architectures are increasingly essential. These intelligent systems are transforming the traditional drug development paradigm, enabling enhanced screening of vast chemical libraries, improved molecular interaction prediction, and increased clinical trial success rates. Additionally, specific uses of machine learning in illness-specific settings, such as Alzheimer's disease, diabetes mellitus, and cardiovascular disorders are mentioned. ML has made it easier to create customized therapies and innovative risk assessment methods, such as phenomapping, in cardiovascular medicine. Data-driven methods are improving patient management and diagnostics in diabetes care, while explainable AI is tackling decision-making transparency. ML algorithms that examine MRI data and biomarker patterns are speeding up early diagnosis for neurodegenerative diseases like Alzheimer's, allowing for prompt therapies. This paper emphasizes how machine learning (ML) can revolutionize precision medicine and generic drug discovery by enabling more patient-specific and data-informed therapeutic actions.